Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Minimizing total weighted tardiness on a single batch process machine with incompatible job families
Computers and Operations Research
Computers and Operations Research
A comparison of mip-based decomposition techniques and VNS approaches for batch scheduling problems
Winter Simulation Conference
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In this paper, we discuss a scheduling problem for a single batch processing machine that is motivated by problems found in semiconductor manufacturing. The jobs belong to different incompatible families. Only jobs of the same family can be batched together. Unequal ready times of the jobs are assumed. The performance measure of interest is the total weighted tardiness (TWT). We design a hybridized grouping genetic algorithm (HGGA) to tackle this problem. In contrast to related work on genetic algorithms (GAs) for similar problems, the representation used in HGGA is based on a variable number of batches. We compare the HGGA with a variable neighborhood search (VNS) technique with respect to solution quality, computational effectiveness, and impact of the initial solution by using randomly generated problem instances. It turns out that the HGGA performs similar to the VNS scheme with respect to solution quality. At the same time, HGGA is slightly more robust with respect to the quality of the initial solution.